Stock Picking Basics
Kerry Back
Simple Valuation Model
Growing perpetuity
- Cash flows \(C_1 = c\), \(C_2 = (1+g)c\), \(C_3 = (1+g)^2c\) and so on forever.
- Discount rate \(r>g\)
- PV is
\[ c\left[\frac{1}{1+r} + \frac{1+g}{(1+r)^2} + \frac{(1+g)^2}{(1+r)^3} + \cdots\right] = \frac{c}{r-g}\]
Gordon growth model
- We want to value cash flows to shareholders
- \(r=\) required return on equity
- Payouts to shareholders = dividends + repurchases - net issues
- Assume earnings, payouts, and book equity all grow at rate \(g<r\).
- Define ROE to be earnings divided by lagged (beginning of year) equity.
- Set \(k =\) payout ratio \(=\) payouts / earnings.
- Equity grows by earnings minus payouts = \((1-k) \times\) earnings.
- Earnings \(=\) ROE \(\times\) lagged equity.
- \(g=\) % change in equity \(=\) growth in equity / lagged equity
\[=\frac{(1-k) \times \text{ROE} \times \text{lagged equity}}{\text{lagged equity}}\]
\[= (1-k) \times \text{ROE}\]
- Value of stock is next year’s payout / \((r-g)\).
- Next year’s payout is \(k\) \(\times\) next year’s earnings.
- Next year’s earnings \(=\) ROE \(\times\) current book equity.
- Theoretical price-to-book \(=\) market-to-book
\[=\left.\frac{k \times \text{ROE} \times \text{book equity}}{r-(1-k)\times \text{ROE}}\right/ \text{book equity}\]
\[=\frac{k \times \text{ROE}}{r-(1-k)\times \text{ROE}}\]
Dupont Analysis
\[\text{ROE} = \frac{\text{Net Income}}{\text{Sales}} \times \frac{\text{Sales}}{\text{Lagged Assets}} \] \[\times \frac{\text{Lagged Assets}}{\text{Lagged Equity}}\]
\[= \text{Profit Margin} \times \text{Asset Turnover}\] \[ \times \text{Leverage}\]
Security Analysis
Sell side and buy side
- Sell-side analysts work for brokerage firms and provide research to brokerage clients.
- They are a cost center and research is provided free to generate business (= commissions or advising fees).
- Buy-side analysts work for investment funds who use the research to pick stocks.
Technical versus fundamental
- Fundamental analysts forecast important ratios and growth rates to produce earnings forecasts and price targets as in Gordon/Dupont.
- Technical analysts use past prices to generate recommendations.
Technical analysis examples
- Support and resistance levels.
- Previous minimum (support) and maximum (resistance) stock prices are regarded as difficult to breach.
- But if breached, the trend is expected to continue.
- Previous minimum (support) and maximum (resistance) stock prices are regarded as difficult to breach.
- Moving averages: buy when price rises above moving average and sell when it falls below.
- Chart patterns (head and shoulders, …)
Quantitative Investing
- Quantitative investing means using quantifiable signals to pick stocks and/or to time the market.
- Signals can include ratios and growth rates used by fundamental analysts and price signals used by technical analysts.
- Signals can also include
- insider trades, short interest, …
- sentiment analysis of social media, traditional media, and company announcements
- satellite and drone image data, and …
Efficient Markets Hypothesis
- All relevant information is already impounded into prices.
- Fundamental analysis is futile.
- Technical analysis is futile.
- Higher expected returns come only with higher risks: arket risk (beta) and/or other types of risks (oil price, …)
Counter-argument
- Not all investors are smart
- Smart investors may not scoop up all opportunities
- Limited capital
- Costs of trading
- For example, an investor who shorts risks running out of capital from margin calls before being eventually right.
- More likely to be opportunities among smaller stocks, which are difficult for large investors to trade.
Smart beta (factor) investing
- Groups of stocks with certain characteristics seem to have higher expected returns.
- These stocks also usually tend to move together.
- Maybe they are exposed to some risk that some investors regard as undesirable.
- Maybe you want to take on that risk to get the return.
- The return of the group of stocks is called a factor.
- Investing in the factor means you will be correlated with the factor.
- So, if we regress your return on the factor, you will have a positive slope coefficient (beta).
- Hence the name “smart beta.”
- Example: Fama-French factors: Small Minus Big, High book-to-market Minus Low book-to-market, Conservative Minus Agressive, Robust Minus Weak.
- The “exposed to some risk” story is a way to reconcile factor investing with the efficient markets hypothesis.
- It is also possible that stocks are just mispriced in systematic ways.
- For example, there is evidence that analysts recognize that “quality” stocks are worth more than “junk” stocks, but they underestimate how much more.
Industry examples
Some data 
- Sort into quintiles each month.
- Value weighted return of each group
- Re-sort at the beginning of the next period and continue.
Factor investing with machine learning
- Find factors worth investing in.
- Decide how to optimally combine them.
- Using ML, we can in principle throw in lots of characteristics and let the machine decide which are useful, but preprocessing is usually useful.
- Need to backtest, which is a variation of the usual ML train-and-test.
- Gu, Kelly, and Xiu, 2020